{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Padding and Stride"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example in the previous section,we used an input with a height and width of 3 and a convolution kernel with a height and width of 2 to get an output with a height and a width of 2. In general, assuming the input shape is n<sub>h</sub> × n<sub>w</sub> and the convolution kernel window shape is k<sub>h</sub> × k<sub>w</sub> , then the output shape will be (n<sub>h</sub> − k<sub>h</sub> + 1) × (n<sub>w</sub> − k<sub>w</sub> + 1).\n",
    "Therefore, the output shape of the convolutional layer is determined by the shape of the input and the shape of the convolution kernel window. In several cases we might want to change the dimensionality of\n",
    "the output:\n",
    "\n",
    "• Multiple layers of convolutions reduce the information available at the boundary, often by much\n",
    "more than what we would want. If we start with a 240x240 pixel image, 10 layers of 5x5 convo-\n",
    "lutions reduce the image to 200x200 pixels, effectively slicing off 30% of the image and with it\n",
    "obliterating anything interesting on the boundaries. Padding mitigates this problem.\n",
    "• In some cases we want to reduce the resolution drastically, e.g. halving it if we think that such\n",
    "a high input dimensionality is not required. In this case we might want to subsample the output.\n",
    "Strides address this.\n",
    "\n",
    "• In some cases we want to increase the resolution, e.g. for image superresolution or for audio\n",
    "generation. Again, strides come to our rescue.\n",
    "\n",
    "• In some cases we want to increase the length gently to a given size (mostly for sentences of variable\n",
    "length or for filling in patches). Padding addresses this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Padding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw so far, convolutions are quite useful. Alas, on the boundaries we encounter the problem\n",
    "that we keep on losing pixels. For any given convolution it’s only a few pixels but this adds up as we\n",
    "discussed above. If the image was larger things would be easier - we could simply record one that’s\n",
    "larger. Unfortunately, that’s not what we get in reality. One solution to this problem is to add extra\n",
    "pixels around the boundary of the image, thus increasing the effective size of the image (the extra pixels\n",
    "typically assume the value 0). In the figure below we pad the 3×5 to increase to 5×7. The corresponding\n",
    "output then increases to a 4×6 matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=\"img/padding.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fig. 6.2: Two-dimensional cross-correlation with padding. The shaded portions are the input and kernel\n",
    "array elements used by the first output element: 0×0 + 0×1 + 0×2 + 0×3 = 0.\n",
    "\n",
    "In general, if a total of p<sub>h</sub> rows are padded on both sides of the height and a total of p<sub>w</sub> columns are\n",
    "padded on both sides of width, the output shape will be (n<sub>h</sub> − k<sub>h</sub> + p<sub>h</sub> + 1) × (n<sub>w</sub> − k<sub>w</sub> + p<sub>w</sub> + 1),\n",
    "This means that the height and width of the output will increase by p<sub>h</sub> and p<sub>w</sub> respectively.\n",
    "\n",
    "In many cases, we will want to set p<sub>h</sub> = k<sub>h</sub> − 1 and p<sub>w</sub> = k<sub>w</sub> − 1 to give the input and output the same\n",
    "height and width. This will make it easier to predict the output shape of each layer when constructing the\n",
    "network. Assuming that k<sub>h</sub> is odd here, we will pad p<sub>h</sub> /2 rows on both sides of the height. If k<sub>h</sub> is even,\n",
    "one possibility is to pad ⌈p<sub>h</sub> /2⌉ rows on the top of the input and ⌊p<sub>h</sub> /2⌋ rows on the bottom. We will pad\n",
    "both sides of the width in the same way.\n",
    "\n",
    "Convolutional neural networks often use convolution kernels with odd height and width values, such as\n",
    "1, 3, 5, and 7, so the number of padding rows or columns on both sides are the same. For any two-\n",
    "dimensional array X, assume that the element in its ith row and jth column is X[i,j]. When the\n",
    "number of padding rows or columns on both sides are the same so that the input and output have the same\n",
    "height and width, we know that the output Y[i,j] is calculated by cross-correlation of the input and\n",
    "convolution kernel with the window centered on X[i,j].\n",
    "\n",
    "In the following example we create a two-dimensional convolutional layer with a height and width of 3,\n",
    "and then assume that the padding number on both sides of the input height and width is 1. Given an input\n",
    "with a height and width of 8, we find that the height and width of the output is also 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 8])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "# We define a convenience function to calculate the convolutional layer. This \n",
    "# function initializes the convolutional layer weights and performs\n",
    "# corresponding dimensionality elevations and reductions on the input and\n",
    "# output\n",
    "def comp_conv2d(conv2d, X):\n",
    "    # (1,1) indicates that the batch size and the number of channels\n",
    "    # (described in later chapters) are both 1\n",
    "    X = X.reshape((1, 1) + X.shape)\n",
    "    Y = conv2d(X)\n",
    "    # Exclude the first two dimensions that do not interest us: batch and\n",
    "    # channel\n",
    "    return Y.reshape(Y.shape[2:])\n",
    "# Note that here 1 row or column is padded on either side, so a total of 2\n",
    "# rows or columns are added\n",
    "conv2d = nn.Conv2d(in_channels=1,out_channels=1,kernel_size=3,padding=1)\n",
    "X = torch.rand(size=(8, 8))\n",
    "comp_conv2d(conv2d, X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the height and width of the convolution kernel are different, we can make the output and input have\n",
    "the same height and width by setting different padding numbers for height and width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 8])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we use a convolution kernel with a height of 5 and a width of 3. The\n",
    "# padding numbers on both sides of the height and width are 2 and 1,\n",
    "# respectively\n",
    "conv2d = nn.Conv2d(in_channels=1,out_channels=1, kernel_size=(5, 3), padding=(2, 1))\n",
    "comp_conv2d(conv2d, X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stride\n",
    "When computing the cross-correlation the convolution window starts from the top-left of the input array,\n",
    "and slides in the input array from left to right and top to bottom. We refer to the number of rows and\n",
    "columns per slide as the stride.\n",
    "\n",
    "In the current example, the stride is 1, both in terms of height and width. We can also use a larger stride.\n",
    "The figure below shows a two-dimensional cross-correlation operation with a stride of 3 vertically and\n",
    "2 horizontally. We can see that when the second element of the first column is output, the convolution\n",
    "window slides down three rows. The convolution window slides two columns to the right when the second\n",
    "element of the first row is output. When the convolution window slides two columns to the right on the\n",
    "input, there is no output because the input element cannot fill the window (unless we add padding)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"img/stride.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fig.6.3: Cross-correlation with strides of 3 and 2 for height and width respectively. The shaded portions\n",
    "are the output element and the input and core array elements used in its computation: 0×0 + 0×1 + 1×2 + 2×3 = 8, 0×0 + 6×1 + 0×2 + 0×3 = 6.\n",
    "\n",
    "In general, when the stride for the height is s<sub>h</sub> and the stride for the width is s<sub>w</sub>,the output shape is ⌊(n<sub>h</sub> − k<sub>h</sub> + p<sub>h</sub> + s<sub>h</sub> ) / s<sub>h</sub> ⌋ × ⌊(n<sub>w</sub> − k<sub>w</sub> + p<sub>w</sub> + s<sub>w</sub> ) / s<sub>w</sub> ⌋.\n",
    "\n",
    "If we set p<sub>h</sub> = k<sub>h</sub> − 1 and p<sub>w</sub> = k<sub>w</sub> − 1,\n",
    "then the output shape will be simplified to <br> ⌊(n<sub>h</sub> + s<sub>h</sub> − 1) / s<sub>h</sub> ⌋ × ⌊(n<sub>w</sub> + s<sub>w</sub> − 1) / s<sub>w</sub> ⌋. \n",
    "\n",
    "Going a step further, if the input height and width are divisible by the strides on\n",
    "the height and width, then the output shape will be (n<sub>h</sub> / s<sub>h</sub> ) × (n<sub>w</sub> / s<sub>w</sub> ).\n",
    "\n",
    "Below, we set the strides on both the height and width to 2, thus halving the input height and width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 4])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv2d = nn.Conv2d(in_channels=1,out_channels=1, kernel_size=3, padding=1, stride=2)\n",
    "comp_conv2d(conv2d, X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will look at a slightly more complicated example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv2d = nn.Conv2d(in_channels=1,out_channels=1, kernel_size=(3, 5), padding=(0, 1), stride=(3, 4))\n",
    "comp_conv2d(conv2d, X).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of brevity, when the padding number on both sides of the input height and width are p<sub>h</sub> and\n",
    "p<sub>w</sub> respectively, we call the padding (p<sub>h</sub> , p<sub>w</sub> ). Specifically, when p<sub>h</sub> = p<sub>w</sub> = p, the padding is p. When\n",
    "the strides on the height and width are s<sub>h</sub> and s<sub>w</sub> , respectively, we call the stride (s<sub>h</sub>,s<sub>w</sub>). Specifically,\n",
    "when s<sub>h</sub> = s<sub>w</sub> = s, the stride is s. By default, the padding is 0 and the stride is 1. In practice we rarely\n",
    "use inhomogeneous strides or padding, i.e. we usually have p<sub>h</sub> = p<sub>w</sub> and s<sub>h</sub> = s<sub>w</sub> ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    " • Padding can increase the height and width of the output. This is often used to give the output the same height and width as the input.\n",
    " \n",
    " • The stride can reduce the resolution of the output, for example reducing the height and width of the output to only 1/n of the height and width of the input (n is an integer greater than 1).\n",
    " \n",
    " • Padding and stride can be used to adjust the dimensionality of the data effectively.\n",
    "\n",
    "## Exercises\n",
    " 1. or the last example in this section, use the shape calculation formula to calculate the output shape to see if it is consistent with the experimental results.\n",
    " \n",
    " \n",
    " 2. Try other padding and stride combinations on the experiments in this section.\n",
    " \n",
    " \n",
    " 3. For audio signals, what does a stride of 2 correspond to?\n",
    " \n",
    " \n",
    " 4. What are the computational benefits of a stride larger than 1."
   ]
  }
 ],
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